Even though most ML-based propagation scenarios correspond to regression tasks, such as those previously described in the paper, classification problems have also been considered. As an example, in [1]}, the authors used high-level information at the receiver, such as PLE, RMS delay, RMS angular spread and others, about various measured as well as simulated communication scenarios. Then, they used these as inputs to classify each environment into urban or rural, each one having also an LOS or NLOS specification. In some classification problems, choosing the number of different classes may not be completely straightforward and the labeling may also be subject to human errors. In [1]}, an environment could have both urban and rural features, therefore, its classification as urban or rural may be misleading. In such cases, unsupervised algorithms may be used to cluster the data. In [1]}, the authors experimented with 2 supervised algorithms, \(k\) -NN and SVM, as well as with 2 unsupervised, the \(k\) -means [4]} and a probabilistic inference model, the Gaussian mixture model (GMM). It is interesting to note that the authors found that the optimal number of clusters for the \(k\) -means algorithm was equal to the number of classifying classes.
